mlsanityData to 7 Oct 2026

α-Geodesical Skew Divergence

arXiv 2103.17060 ↗First posted 31 Mar 2021cs.IT math.IT math.STcited by 6

Not on the map. Why?

Abstract (arXiv)

The asymmetric skew divergence smooths one of the distributions by mixing it, to a degree determined by the parameter λ, with the other distribution. Such divergence is an approximation of the KL divergence that does not require the target distribution to be absolutely continuous with respect to the source distribution. In this paper, an information geometric generalization of the skew divergence called the α-geodesical skew divergence is proposed, and its properties are studied.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 4 of its 45 references that are in the map. First posted on arXiv 31 Mar 2021.

Builds on

4 of 45 references.

References · 4

  1. 2019On a generalization of the Jensen-Shannon divergence and the JS-symmetrization of distances relying on abstract meanscited by 61
  2. 2014Generalized Bhattacharyya and Chernoff upper bounds on Bayes error using quasi-arithmetic meanscited by 10
  3. 2020Annealed Importance Sampling with q-Pathscited by 6
  4. 2019Controllable Dual Skew Divergence Loss for Neural Machine Translationcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 2; 2025: 1.

6 papers.

  1. 2024On permutation-invariant neural networkscited by 10
  2. 2023Information Geometrically Generalized Covariate Shift Adaptationcited by 6
  3. 2024Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergencecited by 3
  4. 2022Information Geometry of Dropout Trainingcited by 2
  5. 2022Fashion-Specific Attributes Interpretation via Dual Gaussian Visual-Semantic Embeddingcited by 1
  6. 2025Graph-Smoothed Bayesian Black-Box Shift Estimator and Its Information Geometrycited by 1